brian2modelfitting.fitter module

class brian2modelfitting.fitter.Fitter(dt, model, input, output, n_samples, input_var=None, output_var=None, threshold=None, reset=None, refractory=None, method=None, param_init=None, penalty=None, use_units=True)

Bases: object

Base Fitter class for model fitting applications.

Creates an interface for model fitting of traces with parameters draw by gradient-free algorithms (through ask/tell interfaces).

Initiates n_neurons = num input traces * num samples, to which drawn parameters get assigned and evaluates them in parallel.

Parameters:

• dt (Quantity) – The size of the time step.
• model (Equations or str) – The equations describing the model.
• input (dic, ndarray or Quantity) – A dictionary given the input variable as the key and a 2D array of shape (n_traces, time steps) as the value, defining the input that will be fed into the model. Note that this variable should be used in the model (e.g. a variable I that is added as a current in the membrane potential equation), but not defined.
• output (dict, Quantity or list) – Recorded output of the model that the model should reproduce. Should be given as a dictionary with the name of the variable as the key and the desired output as the value. The desired output has to be a 2D array of the same shape as the input when fitting traces with TraceFitter, or a list of spike times when fitting spike trains with SpikeFitter. Can also be a list of several output 2D arrays or a single output array if combined with output_var (deprecated use).
• input_var (str) –

  The name of the input variable in the model. Note that this variable should be used in the model (e.g. a variable I that is added as a current in the membrane potential equation), but not defined. .. deprecated:: 0.5

  Use a dictionary for input instead.

• output_var (str or list of str) –

  The name of the output variable in the model or a list of output variables. Only needed when fitting traces with TraceFitter. .. deprecated:: 0.5

  Use a dictionary for output instead.

• n_samples (int) – Number of parameter samples to be optimized over in a single iteration.
• threshold (str, optional) – The condition which produces spikes. Should be a boolean expression as a string.
• reset (str, optional) – The (possibly multi-line) string with the code to execute on reset.
• refractory (str or Quantity, optional) – Either the length of the refractory period (e.g. 2*ms), a string expression that evaluates to the length of the refractory period after each spike (e.g. ‘(1 + rand())*ms’), or a string expression evaluating to a boolean value, given the condition under which the neuron stays refractory after a spike (e.g. ‘v > -20*mV’)
• method (str, optional) – Integration method
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.
• param_init (dict, optional) – Dictionary of variables to be initialized with respective values

best_error

best_objective_errors

best_objective_errors_normalized

best_params

calc_errors(metric)

Abstract method required in all Fitter classes, used for calculating errors

Parameters:metric (Metric children) – Child of Metric class, specifies optimization metric

fit(optimizer, metric=None, n_rounds=1, callback='text', restart=False, online_error=False, start_iteration=None, penalty=None, level=0, **params)

Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.

Parameters:

• optimizer (Optimizer children) – Child of Optimizer class, specific for each library.
• metric (Metric, or dict) – Child of Metric class, specifies optimization metric. In the case of multiple fitted output variables, can either be a single Metric that is applied to all variables, or a dictionary with a Metric for each variable.
• n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
• callback (str or Callable) – Either the name of a provided callback function (text or progressbar), or a custom feedback function func(parameters, errors, best_parameters, best_error, index, additional_info). If this function returns True the fitting execution is interrupted.
• restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
• online_error (bool, optional) – Whether to calculate the squared error between target trace and simulated trace online. Defaults to False.
• start_iteration (int, optional) – A value for the iteration variable at the first iteration. If not given, will use 0 for the first call of fit (and for later calls when restart is specified). Later calls will continue to increase the value from the previous calls.
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time. If not given, will reuse the value specified during Fitter initialization.
• level (int, optional) – How much farther to go down in the stack to find the namespace.
• **params – bounds for each parameter

Returns:

• best_results (dict) – dictionary with best parameter set
• error (float) – error value for best parameter set

generate(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)

Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.

Parameters:

• output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name spikes to record spikes. Can also be a sequence of names to record multiple variables.
• params (dict) – Dictionary of parameters to generate fits for.
• param_init (dict) – Dictionary of initial values for the model.
• iteration (int, optional) – Value for the iteration variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.
• level (int, optional) – How much farther to go down in the stack to find the namespace.

Returns:

Either a 2D Quantity with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given as output_var, then the result is a dictionary with the names of the variable as the key.

Return type:

fit

n_neurons

optimization_iter(optimizer, metric, penalty)

Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.

Parameters:

• optimizer (Optimizer) –
• metric (Metric) –
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.

Returns:

• results (list) – recommended parameters
• parameters (list of list) – drawn parameters
• errors (list) – calculated errors

results(format='list', use_units=None)

Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.

Parameters:

• format (str) – The desired output format. Currently supported: dataframe, list, or dict.
• use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to Tracefitter.use_units, i.e. the value that was specified when the Tracefitter object was created (True by default).

Returns:

‘dataframe’: returns pandas DataFrame without units ‘list’: list of dictionaries ‘dict’: dictionary of lists

Return type:

object

setup_neuron_group(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')

Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.

Parameters:

• n_neurons (int) – number of required neurons
• **namespace – arguments to be added to NeuronGroup namespace

Returns:

neurons – group of neurons

Return type:

NeuronGroup

setup_simulator(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)

class brian2modelfitting.fitter.OnlineTraceFitter(model, input_var, input, output_var, output, dt, n_samples=30, method=None, reset=None, refractory=False, threshold=None, param_init=None, t_start=0. * second, penalty=None)

Bases: brian2modelfitting.fitter.Fitter

best_error

best_objective_errors

best_objective_errors_normalized

best_params

calc_errors(metric=None)

Calculates error in online fashion.To be used inside optim_iter.

fit(optimizer, n_rounds=1, callback='text', restart=False, start_iteration=None, penalty=None, level=0, **params)

Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.

Parameters:

• optimizer (Optimizer children) – Child of Optimizer class, specific for each library.
• metric (Metric, or dict) – Child of Metric class, specifies optimization metric. In the case of multiple fitted output variables, can either be a single Metric that is applied to all variables, or a dictionary with a Metric for each variable.
• n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
• callback (str or Callable) – Either the name of a provided callback function (text or progressbar), or a custom feedback function func(parameters, errors, best_parameters, best_error, index, additional_info). If this function returns True the fitting execution is interrupted.
• restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
• online_error (bool, optional) – Whether to calculate the squared error between target trace and simulated trace online. Defaults to False.
• start_iteration (int, optional) – A value for the iteration variable at the first iteration. If not given, will use 0 for the first call of fit (and for later calls when restart is specified). Later calls will continue to increase the value from the previous calls.
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time. If not given, will reuse the value specified during Fitter initialization.
• level (int, optional) – How much farther to go down in the stack to find the namespace.
• **params – bounds for each parameter

Returns:

• best_results (dict) – dictionary with best parameter set
• error (float) – error value for best parameter set

generate(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)

Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.

Parameters:

• output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name spikes to record spikes. Can also be a sequence of names to record multiple variables.
• params (dict) – Dictionary of parameters to generate fits for.
• param_init (dict) – Dictionary of initial values for the model.
• iteration (int, optional) – Value for the iteration variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.
• level (int, optional) – How much farther to go down in the stack to find the namespace.

Returns:

Either a 2D Quantity with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given as output_var, then the result is a dictionary with the names of the variable as the key.

Return type:

fit

generate_traces(params=None, param_init=None, level=0)

Generates traces for best fit of parameters and all inputs

n_neurons

optimization_iter(optimizer, metric, penalty)

Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.

Parameters:

• optimizer (Optimizer) –
• metric (Metric) –
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.

Returns:

• results (list) – recommended parameters
• parameters (list of list) – drawn parameters
• errors (list) – calculated errors

results(format='list', use_units=None)

Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.

Parameters:

• format (str) – The desired output format. Currently supported: dataframe, list, or dict.
• use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to Tracefitter.use_units, i.e. the value that was specified when the Tracefitter object was created (True by default).

Returns:

‘dataframe’: returns pandas DataFrame without units ‘list’: list of dictionaries ‘dict’: dictionary of lists

Return type:

object

setup_neuron_group(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')

Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.

Parameters:

• n_neurons (int) – number of required neurons
• **namespace – arguments to be added to NeuronGroup namespace

Returns:

neurons – group of neurons

Return type:

NeuronGroup

setup_simulator(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)

class brian2modelfitting.fitter.SpikeFitter(model, input, output, dt, reset, threshold, input_var='I', refractory=False, n_samples=30, method=None, param_init=None, penalty=None, use_units=True)

Bases: brian2modelfitting.fitter.Fitter

best_error

best_objective_errors

best_objective_errors_normalized

best_params

calc_errors(metric)

Returns errors after simulation with SpikeMonitor. To be used inside optim_iter.

fit(optimizer, metric=None, n_rounds=1, callback='text', restart=False, start_iteration=None, penalty=None, level=0, **params)

Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.

Parameters:

• optimizer (Optimizer children) – Child of Optimizer class, specific for each library.
• metric (Metric, or dict) – Child of Metric class, specifies optimization metric. In the case of multiple fitted output variables, can either be a single Metric that is applied to all variables, or a dictionary with a Metric for each variable.
• n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
• callback (str or Callable) – Either the name of a provided callback function (text or progressbar), or a custom feedback function func(parameters, errors, best_parameters, best_error, index, additional_info). If this function returns True the fitting execution is interrupted.
• restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
• online_error (bool, optional) – Whether to calculate the squared error between target trace and simulated trace online. Defaults to False.
• start_iteration (int, optional) – A value for the iteration variable at the first iteration. If not given, will use 0 for the first call of fit (and for later calls when restart is specified). Later calls will continue to increase the value from the previous calls.
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time. If not given, will reuse the value specified during Fitter initialization.
• level (int, optional) – How much farther to go down in the stack to find the namespace.
• **params – bounds for each parameter

Returns:

• best_results (dict) – dictionary with best parameter set
• error (float) – error value for best parameter set

generate(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)

Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.

Parameters:

• output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name spikes to record spikes. Can also be a sequence of names to record multiple variables.
• params (dict) – Dictionary of parameters to generate fits for.
• param_init (dict) – Dictionary of initial values for the model.
• iteration (int, optional) – Value for the iteration variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.
• level (int, optional) – How much farther to go down in the stack to find the namespace.

Returns:

Either a 2D Quantity with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given as output_var, then the result is a dictionary with the names of the variable as the key.

Return type:

fit

generate_spikes(params=None, param_init=None, iteration=1000000000.0, level=0)

Generates traces for best fit of parameters and all inputs

n_neurons

optimization_iter(optimizer, metric, penalty)

Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.

Parameters:

• optimizer (Optimizer) –
• metric (Metric) –
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.

Returns:

• results (list) – recommended parameters
• parameters (list of list) – drawn parameters
• errors (list) – calculated errors

results(format='list', use_units=None)

Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.

Parameters:

• format (str) – The desired output format. Currently supported: dataframe, list, or dict.
• use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to Tracefitter.use_units, i.e. the value that was specified when the Tracefitter object was created (True by default).

Returns:

‘dataframe’: returns pandas DataFrame without units ‘list’: list of dictionaries ‘dict’: dictionary of lists

Return type:

object

setup_neuron_group(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')

Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.

Parameters:

• n_neurons (int) – number of required neurons
• **namespace – arguments to be added to NeuronGroup namespace

Returns:

neurons – group of neurons

Return type:

NeuronGroup

setup_simulator(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)

class brian2modelfitting.fitter.TraceFitter(model, input, output, dt, n_samples=60, input_var=None, output_var=None, method=None, reset=None, refractory=False, threshold=None, param_init=None, penalty=None, use_units=True)

Bases: brian2modelfitting.fitter.Fitter

A Fitter for fitting recorded traces (e.g. of the membrane potential).

Parameters:

• model –
• input_var –
• input –
• output_var –
• output –
• dt –
• n_samples –
• method –
• reset –
• refractory –
• threshold –
• param_init –
• use_units (bool, optional) – Whether to use units in all user-facing interfaces, e.g. in the callback arguments or in the returned parameter dictionary and errors. Defaults to True.

best_error

best_objective_errors

best_objective_errors_normalized

best_params

calc_errors(metric)

Returns errors after simulation with StateMonitor. To be used inside optim_iter.

fit(optimizer, metric=None, n_rounds=1, callback='text', restart=False, start_iteration=None, penalty=None, level=0, **params)

Run the optimization algorithm for given amount of rounds with given number of samples drawn. Return best set of parameters and corresponding error.

Parameters:

• optimizer (Optimizer children) – Child of Optimizer class, specific for each library.
• metric (Metric, or dict) – Child of Metric class, specifies optimization metric. In the case of multiple fitted output variables, can either be a single Metric that is applied to all variables, or a dictionary with a Metric for each variable.
• n_rounds (int) – Number of rounds to optimize over (feedback provided over each round).
• callback (str or Callable) – Either the name of a provided callback function (text or progressbar), or a custom feedback function func(parameters, errors, best_parameters, best_error, index, additional_info). If this function returns True the fitting execution is interrupted.
• restart (bool) – Flag that reinitializes the Fitter to reset the optimization. With restart True user is allowed to change optimizer/metric.
• online_error (bool, optional) – Whether to calculate the squared error between target trace and simulated trace online. Defaults to False.
• start_iteration (int, optional) – A value for the iteration variable at the first iteration. If not given, will use 0 for the first call of fit (and for later calls when restart is specified). Later calls will continue to increase the value from the previous calls.
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time. If not given, will reuse the value specified during Fitter initialization.
• level (int, optional) – How much farther to go down in the stack to find the namespace.
• **params – bounds for each parameter

Returns:

• best_results (dict) – dictionary with best parameter set
• error (float) – error value for best parameter set

generate(output_var=None, params=None, param_init=None, iteration=1000000000.0, level=0)

Generates traces for best fit of parameters and all inputs. If provided with other parameters provides those.

Parameters:

• output_var (str or sequence of str) – Name of the output variable to be monitored, or the special name spikes to record spikes. Can also be a sequence of names to record multiple variables.
• params (dict) – Dictionary of parameters to generate fits for.
• param_init (dict) – Dictionary of initial values for the model.
• iteration (int, optional) – Value for the iteration variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when generating traces/spikes for given parameters.
• level (int, optional) – How much farther to go down in the stack to find the namespace.

Returns:

Either a 2D Quantity with the recorded output variable over time, with shape <number of input traces> × <number of time steps>, or a list of spike times for each input trace. If several names were given as output_var, then the result is a dictionary with the names of the variable as the key.

Return type:

fit

generate_traces(params=None, param_init=None, iteration=1000000000.0, level=0)

Generates traces for best fit of parameters and all inputs

n_neurons

optimization_iter(optimizer, metric, penalty)

Function performs all operations required for one iteration of optimization. Drawing parameters, setting them to simulator and calulating the error.

Parameters:

• optimizer (Optimizer) –
• metric (Metric) –
• penalty (str, optional) – The name of a variable or subexpression in the model that will be added to the error at the end of each iteration. Note that only the term at the end of the simulation is taken into account, so this term should not be varying in time.

Returns:

• results (list) – recommended parameters
• parameters (list of list) – drawn parameters
• errors (list) – calculated errors

refine(params=None, metric=None, callback='text', calc_gradient=False, optimize=True, iteration=1000000000.0, level=0, **kwds)

Refine the fitting results with a sequentially operating minimization algorithm. The least_squares algorithm from scipy.optimize. Has to be called after fit, but a call with n_rounds=0 is enough.

Parameters:

• params (dict, optional) – A dictionary with the parameters to use as a starting point for the refinement. If not given, the best parameters found so far by fit will be used.
• metric (MSEMetric or dict, optional) – Optimization metrics to use. Since the refinement only supports mean-squared-error metrics, this is only useful to provide the t_start/t_weights/normalization values. In the case of multiple fitted output variables, can either be a single MSEMetric that is applied to all variables, or a dictionary with a MSEMetric for each variable. If not given, will reuse the metrics of a previous fit call.
• callback (str or Callable) – Either the name of a provided callback function (text or progressbar), or a custom feedback function func(parameters, errors, best_parameters, best_error, index). If this function returns True the fitting execution is interrupted.
• calc_gradient (bool, optional) – Whether to add “sensitivity variables” to the equation that track the sensitivity of the equation variables to the parameters. This information will be used to pass the local gradient of the error with respect to the parameters to the optimization function. This can lead to much faster convergence than with an estimated gradient but comes at the expense of additional computation. Defaults to False.
• optimize (bool, optional) – Whether to remove sensitivity variables from the equations that do not evolve if initialized to zero (e.g. dS_x_y/dt = -S_x_y/tau would be removed). This avoids unnecessary computation but will fail in the rare case that such a sensitivity variable needs to be initialized to a non-zero value. Only taken into account if calc_gradient is True. Defaults to True.
• iteration (int, optional) – Value for the iteration variable provided to the simulation. Defaults to a high value (1e9). This is based on the assumption that the model implements some coupling of the fitted variable to the target variable, and that this coupling inversely depends on the iteration number. In this case, one would usually want to switch off the coupling when refining the solution.
• level (int, optional) – How much farther to go down in the stack to find the namespace.
• kwds – Additional arguments can overwrite the bounds for individual parameters (if not given, the bounds previously specified in the call to fit will be used). All other arguments will be passed on to least_squares.

Returns:

• parameters (dict) – The parameters at the end of the optimization process as a dictionary.
• result (scipy.optimize.OptimizeResult) – The result of the optimization process.

Notes

There is no support for specifying a Metric, the given output trace(s) will be subtracted from the simulated trace(s) and passed on to the minimization algorithm which will internally calculate the sum of squares.

results(format='list', use_units=None)

Returns all of the gathered results (parameters and errors). In one of the 3 formats: ‘dataframe’, ‘list’, ‘dict’.

Parameters:

• format (str) – The desired output format. Currently supported: dataframe, list, or dict.
• use_units (bool, optional) – Whether to use units in the results. If not specified, defaults to Tracefitter.use_units, i.e. the value that was specified when the Tracefitter object was created (True by default).

Returns:

‘dataframe’: returns pandas DataFrame without units ‘list’: list of dictionaries ‘dict’: dictionary of lists

Return type:

object

setup_neuron_group(n_neurons, namespace, calc_gradient=False, optimize=True, online_error=False, name='neurons')

Setup neuron group, initialize required number of neurons, create namespace and initialize the parameters.

Parameters:

• n_neurons (int) – number of required neurons
• **namespace – arguments to be added to NeuronGroup namespace

Returns:

neurons – group of neurons

Return type:

NeuronGroup

setup_simulator(network_name, n_neurons, output_var, param_init, calc_gradient=False, optimize=True, online_error=False, level=1)

brian2modelfitting.fitter.get_full_namespace(additional_namespace, level=0)

brian2modelfitting.fitter.get_param_dic(params, param_names, n_traces, n_samples)

Transform parameters into a dictionary of appropriate size From list of dictionaries to dictionary of lists, with variables repeated for each trace

brian2modelfitting.fitter.get_sensitivity_equations(group, parameters, namespace=None, level=1, optimize=True)

Get equations for sensitivity variables.

Parameters:

• group (NeuronGroup) – The group of neurons that will be simulated.
• parameters (list of str) – Names of the parameters that are fit.
• namespace (dict, optional) – The namespace to use.
• level (int, optional) – How much farther to go down in the stack to find the namespace.
• optimize (bool, optional) – Whether to remove sensitivity variables from the equations that do not evolve if initialized to zero (e.g. dS_x_y/dt = -S_x_y/tau would be removed). This avoids unnecessary computation but will fail in the rare case that such a sensitivity variable needs to be initialized to a non-zero value. Defaults to True.

Returns:

sensitivity_eqs – The equations for the sensitivity variables.

Return type:

Equations

brian2modelfitting.fitter.get_sensitivity_init(group, parameters, param_init)

Calculate the initial values for the sensitivity parameters (necessary if initial values are functions of parameters).

Parameters:

• group (NeuronGroup) – The group of neurons that will be simulated.
• parameters (list of str) – Names of the parameters that are fit.
• param_init (dict) – The dictionary with expressions to initialize the model variables.

Returns:

sensitivity_init – Dictionary of expressions to initialize the sensitivity parameters.

Return type:

dict

brian2modelfitting.fitter.get_spikes(monitor, n_samples, n_traces)

Get spikes from spike monitor change format from dict to a list, remove units.

brian2modelfitting.fitter.setup_fit()

Function sets up simulator in one of the two available modes: runtime or standalone. The Simulator that will be used depends on the currently set Device. In the case of CPPStandaloneDevice, the device will also be reset if it has already run a simulation.

Returns:simulator Return type:Simulator